Practice Applications in AdTech, Recommender Systems - 9.9.5 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.9.5 - Applications in AdTech, Recommender Systems

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does MAB stand for?

πŸ’‘ Hint: Think about a slot machine with multiple options.

Question 2

Easy

Name a use case of MAB in AdTech.

πŸ’‘ Hint: Consider how ads appear on websites.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary goal of Multi-Armed Bandits in AdTech?

  • Maximize revenue
  • Minimize costs
  • Ensure equality
  • Maximize user engagement

πŸ’‘ Hint: Think about what keeps users coming back.

Question 2

True or False: MAB is only useful for determining pricing strategies in AdTech.

  • True
  • False

πŸ’‘ Hint: Consider the broader applications of this approach.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Imagine you run a digital marketing campaign with three ads showing vastly different engagement rates. Design a MAB-based strategy that lets you explore new ads while still pushing the current best performer.

πŸ’‘ Hint: Consider the percentage splits for the exploration and exploitation phases.

Question 2

How can a recommender system use MAB to handle seasonal changes in user preferences, like the transition from summer to winter clothing?

πŸ’‘ Hint: Think about the timing of user engagement with products.

Challenge and get performance evaluation